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Voxel-based approach for disease detection and evolution

a disease detection and evolution technology, applied in the field of systems and methods for monitoring tissue regions, can solve the problem that contrast changes in images taken over time may be difficult to detect by traditional qualitative visual assessmen

Active Publication Date: 2015-06-09
RGT UNIV OF MICHIGAN
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The present patent describes a method for analyzing images of a body to predict the development of a disease. The method involves collecting image data sets of different regions of the body and registering them to create a combined image data set. This combined image data set includes co-registered voxels that have the signal values of both image data sets. A threshold significance level is determined for the difference between the combined voxels, and the volume of voxels that exceed the threshold level is quantified. A parametric response map is then created based on the combined image data set and the threshold significance level, which is used to segment the map data into at least one region where the changes in signal value between the voxels exceed the threshold level. This method improves the accuracy of predicting disease progression, even in the presence of tumor heterogeneity during treatment.

Problems solved by technology

However, the contrast changes in images taken over time may be difficult to detect by traditional qualitative visual assessment, even for the trained professional.

Method used

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  • Voxel-based approach for disease detection and evolution

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[0031]Methods of the present disclosure were demonstrated in a cohort of 14 glioma patients and compared to standard MRI-based criteria of clinical progression. The fourteen patients with pathologically proven grade III / IV gliomas were enrolled on a protocol of intra-treatment MRI. MRI scans were performed before and during treatment (every 2 months) until tumor recurrence was identified by Macdonald Criteria. All images were acquired on 1.5T or 3T MRI scanners. The MR1 protocol included fluid-attenuated inversion recovery imaging (FLAIR) and contrast-enhanced (Gd-DTPA) T1-weighted imaging. To avoid variability between scanners, subjects used the original scanner for all subsequent scans.

[0032]Subsequent to voxel-based analysis, FLAIR images were normalized to the mean signal intensity of white matter tracks (rFLAIR). All image data was registered to pre-treatment Gd-enhanced T1-weighted images using mutual information as an objective function and Nelder-Mead simplex as an optimizer...

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System

[0038]FIG. 3 is a block diagram of an example computer system 1000 on which a PRM system may operate, in accordance with the described embodiments. The computer system 1000 may be a PRM system, for example. The computer system 1000 includes a computing device in the form of a computer 1010 that may include, but is not limited to, a processing unit 1020, a system memory 1030, and a system bus 1021 that couples various system components including the system memory to the processing unit 1020. The system bus 1021 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include the Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (Pa) bus (also known as Mezzanine ...

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Abstract

A voxel-based technique is provided for performing quantitative imaging and analysis of tissue image data. Serial image data is collected for tissue of interest at different states of the issue. The collected image data may be normalized, after which the registered image data is analyzed on a voxel-by-voxel basis, thereby retaining spatial information for the analysis. Various thresholds are applied to the registered tissue data to predict or determine the evolution of a disease state, such as brain cancer, for example.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority to U.S. Provisional Application No. 61 / 563,333, entitled “Voxel-Based Approach for Disease Detection and Evolution,” filed Nov. 23, 2011, which is hereby incorporated herein in their entirety.STATEMENT OF GOVERNMENTAL INTEREST[0002]This invention was made with government support under CA085878 awarded by the National Institutes of Health. The government has certain rights in the invention.FIELD OF THE INVENTION[0003]The present disclosure relates to novel and advantageous systems and methods for monitoring tissue regions and, more particularly, to systems and methods for determining or predicting changes in tissue regions over a period of time, for example, during patient diagnosis or treatment.BACKGROUND[0004]In current standard medical practice, imaging modalities are commonly used to rapidly acquire images to provide qualitative information on the state of a tissue or disease process. In clinical practi...

Claims

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Application Information

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Patent Type & Authority Patents(United States)
IPC IPC(8): G06K9/00G06T7/00A61B5/00A61B5/055
CPCG06T7/0012G06T7/0016A61B5/4842A61B5/742G06T2207/10088G06T2207/30016G06T2207/30096A61B5/055
Inventor ROSS, BRIAN D.CHENEVERT, THOMAS L.GALBAN, CRAIGLEMASSON, BENJAMIN
Owner RGT UNIV OF MICHIGAN
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